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Title: Authoritative subspecies diagnosis tool for European honey bees based on ancestry informative SNPs
Authors: Momeni, Jamal
Parejo, Melanie
Nielsen, Rasmus O.
Langa, Jorge
Montes, Iratxe
Papoutsis, Laetitia
Farajzadeh, Leila
Bendixen, Christian
Căuia, Eliza
Charrière, Jean-Daniel
Coffey, Mary F.
Costa, Cecilia
Dall’Olio, Raffaele
De la Rúa, Pilar
Drazic, M. Maja
Filipi, Janja
Galea, Thomas
Golubovski, Miroljub
Gregorc, Ales
Grigoryan, Karina
Hatjina, Fani
Ilyasov, Rustem
Ivanova, Evgeniya
Janashia, Irakli
Kandemir, Irfan
Karatasou, Aikaterini
Kekecoglu, Meral
Kezic, Nikola
Matray, Enikö Sz.
Mifsud, David
Moosbeckhofer, Rudolf
Nikolenko, Alexei G.
Papachristoforou, Alexandros
Petrov, Plamen
Pinto, M. Alice
Poskryakov, Aleksandr V.
Sharipov, Aglyam Y.
Siceanu, Adrian
Soysal, M. Ihsan
Uzunov, Aleksandar
Zammit-Mangion, Marion
Vingborg, Rikke
Bouga, Maria
Kryger, Per
Meixner, Marina D.
Estonba, Andone
Keywords: Insects -- Malta
Hymenoptera -- Malta
Apidae -- Malta
Apis (Insects) -- Malta
Honeybee -- Malta
Honeybee -- Conservation
Bees -- Conservation
Biodiversity -- Malta
Issue Date: 2021
Publisher: BMC
Citation: Momeni, J., Parejo, M., Nielsen, R. O., Langa, J., Montes, I., Papoutsis, L., ... & Estonba, A. (2021). Authoritative subspecies diagnosis tool for European honey bees based on ancestry informative SNPs. BMC Genomics, 22(1), 1-12.
Abstract: Background: With numerous endemic subspecies representing four of its five evolutionary lineages, Europe holds a large fraction of Apis mellifera genetic diversity. This diversity and the natural distribution range have been altered by anthropogenic factors. The conservation of this natural heritage relies on the availability of accurate tools for subspecies diagnosis. Based on pool-sequence data from 2145 worker bees representing 22 populations sampled across Europe, we employed two highly discriminative approaches (PCA and FST) to select the most informative SNPs for ancestry inference.
Results: Using a supervised machine learning (ML) approach and a set of 3896 genotyped individuals, we could show that the 4094 selected single nucleotide polymorphisms (SNPs) provide an accurate prediction of ancestry inference in European honey bees. The best ML model was Linear Support Vector Classifier (Linear SVC) which correctly assigned most individuals to one of the 14 subspecies or different genetic origins with a mean accuracy of 96.2% ± 0.8 SD. A total of 3.8% of test individuals were misclassified, most probably due to limited differentiation between the subspecies caused by close geographical proximity, or human interference of genetic integrity of reference subspecies, or a combination thereof.
Conclusions: The diagnostic tool presented here will contribute to a sustainable conservation and support breeding activities in order to preserve the genetic heritage of European honey bees.
Appears in Collections:Scholarly Works - InsESRSF

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